APESTNet with Mask R-CNN for Liver Tumor Segmentation and Classification.

Diagnosis and treatment of hepatocellular carcinoma or metastases rely heavily on accurate segmentation and classification of liver tumours. However, due to the liver tumor's hazy borders and wide range of possible shapes, sizes, and positions, accurate and automatic tumour segmentation and classification remains a difficult challenge. With the advancement of computing, new models in artificial intelligence have evolved.

: a compact automated fluorescence microscope for cell counting applications in low resource settings.

Fluorescence microscopy has widespread applications across biological sciences. It has been routinely used for cell counting, which provides a preliminary diagnostic test for many infectious diseases. Conventional fluorescence microscopes are bulky, expensive, time-intensive and laborious.

Microstructural changes in silicon induced by patterning with focused ion beams of Ga, Si and Au.

We use focused beams of Ga(+), Au(+) and Si(++) ions to induce local microstructural changes in single crystal silicon. The ions were delivered as single spot pulses into thin Si membranes that could subsequently be imaged and annealed in situ in a transmission electron microscope. For each ion, the focused ion beam implantation created an array of amorphous regions in the crystalline membrane.